def run_candidates(velscale, bands):
""" Run lector on candidates. """
wdir = os.path.join(home, "data/candidates")
os.chdir(wdir)
specs = sorted([x for x in os.listdir(wdir) if x.endswith(".fits")])
obsres = hydra_resolution()
offset, offerr = lick_offset()
lickout = []
for spec in specs:
ppfile = "logs_ssps/{0}".format(spec.replace(".fits", ""))
if not os.path.exists(ppfile + ".pkl"):
print "Skiping spectrum: ", spec
continue
print ppfile
pp = ppload("logs_ssps/{0}".format(spec.replace(".fits", "")))
pp = pPXF(spec, velscale, pp)
galaxy = pf.getdata(spec)
w = wavelength_array(spec, axis=1, extension=0)
if pp.ncomp > 1:
sol = pp.sol[0]
else:
sol = pp.sol
if pp.ncomp == 1:
csp = pp.star.dot(pp.w_ssps) # composite stellar population
else:
csp = pp.star[:,:-pp.ngas].dot(pp.w_ssps)
######################################################################
# Produce bestfit templates convolved with LOSVD/redshifted
best_unbroad = pp.poly + pp.mpoly * losvd_convolve(csp,
np.array([sol[0], velscale/10.]), velscale)
best_broad = pp.poly + pp.mpoly * losvd_convolve(csp,
sol, velscale)
##################################################################
# Interpolate bestfit templates to obtain linear dispersion
b0 = interp1d(pp.w, best_unbroad, kind="linear",
fill_value="extrapolate", bounds_error=False)
b1 = interp1d(pp.w, best_broad, kind="linear",
fill_value="extrapolate", bounds_error=False)
sky = interp1d(pp.w, pp.bestsky, kind="linear",
fill_value="extrapolate", bounds_error=False)
emission = interp1d(pp.w, pp.gas, kind="linear",
fill_value="extrapolate", bounds_error=False)
best_unbroad = b0(w)
best_broad = b1(w)
######################################################################
# Test plot
# plt.plot(w, best_unbroad, "-b")
# plt.plot(w, best_broad, "-r")
# plt.plot(w, galaxy - sky(w), "-k")
# plt.show()
#######################################################################
# Broadening to Lick system
galaxy = lector.broad2lick(w, galaxy - sky(w) - emission(w), obsres(w),
vel=sol[0])
best_unbroad = lector.broad2lick(w, best_unbroad,
3.7, vel=sol[0])
best_broad = lector.broad2lick(w, best_broad, 3.7,
vel=sol[0])
##################################################################
lick, lickerr = lector.lector(w, galaxy, np.ones_like(w), bands,
vel=sol[0])
lick_unb, tmp = lector.lector(w, best_unbroad,
np.ones_like(w), bands, vel=sol[0])
lick_br, tmp = lector.lector(w, best_broad,
np.ones_like(w), bands, vel=sol[0])
lickc = correct_lick(bands, lick, lick_unb, lick_br) + offset
######################################################################
# Plot to check if corrections make sense
if False:
fig = plt.figure(1)
ax = plt.subplot(111)
ax.plot(lick, "ok")
ax.plot(lick_unb, "xb")
ax.plot(lick_br, "xr")
ax.plot(lick - (lick_br - lick_unb), "+k", ms=10)
ax.plot(lick * lick_unb / lick_br, "xk", ms=10)
ax.plot(lickc - offset, "o", c="none", markersize=10, mec="y")
ax.set_xticks(np.arange(25))
ax.set_xlim(-1, 25)
labels = np.loadtxt(bands, usecols=(0,), dtype=str).tolist()
labels = [x.replace("_", " ") for x in labels]
ax.set_xticklabels(labels, rotation=90)
plt.show()
######################################################################
# Storing results
lickc = ["{0:.5g}".format(x) for x in lickc]
line = "".join(["{0:30s}".format(spec)] + \
["{0:12s}".format(x) for x in lickc])
lickout.append(line)
######################################################################
# Saving to file
with open("lick.txt", "w") as f:
f.write("\n".join(lickout))
def run_candidates_mc(velscale, bands, nsim=50):
""" Run MC to calculate errors on Lick indices. """
wdir = os.path.join(home, "data/candidates")
os.chdir(wdir)
specs = sorted([x for x in os.listdir(wdir) if x.endswith(".fits")])
offset, offerr = lick_offset()
lickout = []
for spec in specs:
try:
ppfile = "logs_ssps/{0}".format(spec.replace(".fits", ""))
if not os.path.exists(ppfile + ".pkl"):
print "Skiping spectrum: ", spec
continue
print ppfile
pp = ppload("logs_ssps/{0}".format(spec.replace(".fits", "")))
pp = pPXF(spec, velscale, pp)
ppkin = ppload("logs/{0}".format(spec.replace(".fits", "")))
ppkin = pPXF(spec, velscale, ppkin)
w = wavelength_array(spec, axis=1, extension=0)
if pp.ncomp > 1:
sol = ppkin.sol[0]
error = ppkin.error[0]
else:
sol = ppkin.sol
error = ppkin.error
###################################################################
# Produces composite stellar population of reference
if pp.ncomp == 1:
csp = pp.star.dot(pp.w_ssps)
else:
csp = pp.star[:,:-pp.ngas].dot(pp.w_ssps)
###################################################################
# Make unbroadened bestfit and measure Lick on it
best_unbroad_ln = pp.poly + pp.mpoly * losvd_convolve(csp,
np.array([sol[0], velscale/10.]), velscale)
b0 = interp1d(pp.w, best_unbroad_ln, kind="linear",
fill_value="extrapolate", bounds_error=False)
best_unbroad_lin = b0(w)
best_unbroad_lin = lector.broad2lick(w, best_unbroad_lin,
3.6, vel=sol[0])
lick_unb, tmp = lector.lector(w, best_unbroad_lin,
np.ones_like(w), bands, vel=sol[0])
###################################################################
# Setup simulations
vpert = np.random.normal(sol[0], error[0], nsim)
sigpert = np.random.normal(sol[1], error[1], nsim)
h3pert = np.random.normal(sol[2], error[2], nsim)
h4pert = np.random.normal(sol[3], error[3], nsim)
licksim = np.zeros((nsim, 25))
###################################################################
for i, (v,s,h3,h4) in enumerate(zip(vpert, sigpert, h3pert, h4pert)):
solpert = np.array([v,s,h3,h4])
noise = np.random.normal(0., pp.noise, len(w))
best_broad_ln = pp.poly + pp.mpoly * losvd_convolve(csp,
solpert, velscale)
b1 = interp1d(pp.w, best_broad_ln, kind="linear",
fill_value="extrapolate", bounds_error=False)
best_broad_lin = b1(w)
###############################################################
# Broadening to Lick system
best_broad_lin = lector.broad2lick(w, best_broad_lin, 3.6,
vel=solpert[0])
lick_br, tmp = lector.lector(w, best_broad_lin,
np.ones_like(w), bands, vel=solpert[0])
lick, lickerr = lector.lector(w, best_broad_lin + noise,
np.ones_like(w), bands, vel=sol[0])
licksim[i] = correct_lick(bands, lick, lick_unb, lick_br) + \
offset
stds = np.zeros(25)
for i in range(25):
stds[i] = np.std(sigma_clip(licksim[:,i], sigma=5))
stds = np.sqrt(stds**2 + offerr**2)
###################################################################
# Storing results
lickc = ["{0:.5g}".format(x) for x in stds]
line = "".join(["{0:35s}".format(spec)] + \
["{0:12s}".format(x) for x in lickc])
lickout.append(line)
###################################################################
except:
print "Problem with spectrum", spec
continue
# Saving to file
with open("lickerr_mc{0}.txt".format(nsim), "w") as f:
f.write("\n".join(lickout))
def run_standard_stars(velscale, bands):
""" Run lector on standard stars to study instrumental dependencies. """
stars_dir = os.path.join(home, "data/standards")
table = os.path.join(tables_dir, "lick_standards.txt")
ids = np.loadtxt(table, usecols=(0,), dtype=str).tolist()
lick_ref = np.loadtxt(table, usecols=np.arange(1,26))
ref, obsm, obsa = [], [], []
res = hydra_resolution()
for night in nights:
os.chdir(os.path.join(stars_dir, night))
stars = [x for x in os.listdir(".") if x.endswith(".fits")]
for star in stars:
ppfile = "logs/{0}".format(star.replace(".fits", ""))
if not os.path.exists(ppfile + ".pkl"):
continue
name = star.split(".")[0].upper()
if name not in ids:
continue
print name
idx = ids.index(name)
lick_star = lick_ref[idx]
pp = ppload("logs/{0}".format(star.replace(".fits", "")))
pp = pPXF(star, velscale, pp)
mpoly = np.interp(pp.wtemp, pp.w, pp.mpoly)
spec = pf.getdata(star)
w = wavelength_array(star, axis=1, extension=0)
best_unbroad_v0 = mpoly * pp.star.dot(pp.w_ssps)
best_broad_v0 = losvd_convolve(best_unbroad_v0,
np.array([0., pp.sol[1]]), velscale)
##################################################################
# Interpolate bestfit templates to obtain linear dispersion
b0 = interp1d(pp.wtemp, best_unbroad_v0, kind="linear",
fill_value="extrapolate", bounds_error=False)
b1 = interp1d(pp.wtemp, best_broad_v0, kind="linear",
fill_value="extrapolate", bounds_error=False)
best_unbroad_v0 = b0(w)
best_broad_v0 = b1(w)
#################################################################
# Broadening to Lick system
spec = lector.broad2lick(w, spec, res(w), vel=pp.sol[0])
best_unbroad_v0 = lector.broad2lick(w, best_unbroad_v0,
3.6, vel=0.)
best_broad_v0 = lector.broad2lick(w, best_broad_v0, 3.6,
vel=0.)
# plt.plot(w, spec, "-k")
# plt.plot(w, best_broad_v0, "-r")
# plt.show()
##################################################################
lick, lickerr = lector.lector(w, spec, np.ones_like(w), bands,
vel=pp.sol[0])
lick_unb, tmp = lector.lector(w, best_unbroad_v0,
np.ones_like(w), bands, vel=0.)
lick_br, tmp = lector.lector(w, best_broad_v0,
np.ones_like(w), bands, vel=0.)
lickm = multi_corr(lick, lick_unb, lick_br)
licka = add_corr(lick, lick_unb, lick_br)
ref.append(lick_star)
obsm.append(lickm)
obsa.append(licka)
with open(os.path.join(tables_dir, "stars_lick_val_corr.txt"), "w") as f:
np.savetxt(f, np.array(ref))
with open(os.path.join(tables_dir, "stars_lick_obs_mcorr.txt"), "w") as f:
np.savetxt(f, np.array(obsm))
with open(os.path.join(tables_dir, "stars_lick_obs_acorr.txt"), "w") as f:
np.savetxt(f, np.array(obsa))
return
